Short-Term Reactive Load Forecasting Based on a Hybrid Machine Learning Model

With the improvement of people's living standards and the access to new energy sources such as rooftop photovoltaics, the variety of electricity consumption on the customer side has increased dramatically, placing high demands on the reactive power compensation configuration, regulation, and control capabilities at all levels of the distribution network. Predicting reactive loads directly impacts the quality of power supply and is also an integral part of reactive power optimization in the grid. With the popularity of smart energy measurement devices, large-scale historical reactive load data is uploaded to the dispatch control system. This paper proposes a hybrid reactive load prediction model based on a machine learning framework. Appropriate error parameters are selected, and the model is compared with a traditional time series prediction model to verify that the model can achieve better prediction results.